Research and development isn't dead, it's broken

No one should have been surprised when the Office of the Legislative Auditor found the Minnesota research-and-development tax credit to be a net drain on the state. Business professor and author Anne Marie Knott sure wasn’t.

Knott saw news of our auditor’s report on a visit to Minnesota this month, and she knew without checking that the tax credit generated more R & D spending. It always does. What rarely follows, though, is much gain in productivity or any other economic measure we care about.

In unpacking her argument against R & D tax credits, however, she’s making a far more important point than simply that tax incentive policies like Minnesota’s don’t really work. Voting these into law is another symptom of the real problem, of people being unable to think clearly when it comes to trying to jump-start innovation.

In a book coming out next month called “How Innovation Really Works,” Knott lumps R & D tax credits in with a long list of other misconceptions, questioning the conventional wisdom of strategies like only chasing radical ideas or looking outside a company for new ideas, known as “open innovation.”

Yet she’s also hopeful. The conventional approach of having your own team of engineers and marketers solve problems still works. What has stalled innovation is mostly having executives routinely misunderstand the value of what they are getting from R & D spending. In other words, the innovation problem seems fixable.

Knott, who teaches strategy at Washington University in St. Louis, knows she’s a rare optimist. It’s now common to hear how we have run out of big ideas, as the Wall Street Journal recently reported. “My answer that is no, there is plenty of opportunity,” she said. “Firms have just gotten worse at the R & D they do.”

It could be tougher to make big leaps in many markets, she said, yet a booming industry isn’t the only way to generate good outcomes from R & D. She ranked the R & D track records of American public companies using her own measurement tool and found that nine of the top 50 most-productive companies are the only representatives on the list from their industries.

It may not be clear what those nine companies may be doing right, but Knott provides page after page of what companies who aren’t in the top 50 have probably done wrong.

Outsource R & D to clever consultants? “One of the most destructive things firms do,” Knott said, as the basic technical know-how of the company invariably seeps away.

Look to the stock price for clues on R & D direction? “That’s just a terrible mechanism for feedback,” Knott said. “Investors know even less than you do about what the right things to do are.”

Executives interested in doing better have actually tried to measure their returns from R & D work, she said, with mixed results. The total number of patents and patent applications is a great example of information that seems like it should be important but often isn’t, in part because a lot of the most-profitable innovations never get patented.

She calls the approach she’s advocating to measure R & D productivity the “research quotient.” The concept rests on an old idea in economics that there’s always a perfect recipe of ingredients — in economics called “inputs” — to produce the best possible outcome. It’s usually taught to students by asking them to figure out the best trade-off between labor and capital.

Think of it as the choice a house builder has. It may actually be possible to build a house using very little labor, but that will cost a lot in tools and materials. The builder probably needs to find a more-profitable recipe that calls for using more labor.

To this kind of formula Knott added R & D spending as another input. The financial information in the annual report may be all that’s necessary to determine R & D productivity. With more math it becomes possible to predict additional sales from an increase in R & D spending, until the best recipe emerges.

This approach works across all industries and across all divisions. And if one division is getting great R & D productivity and the others aren’t, maybe stop funding them.

Her R & D measure is one solution to a problem she has thought about for years. One big reason she went back to school to become a professor in the first place was because she lacked the right tool in the 1980s to show General Motors managers just how they were ruining the company she worked for, Hughes Aircraft Co.

The GM bureaucracy had mastered how to sort the thousands of proposed engineering projects every year by expected returns, thus identifying the ones that should get funded. Carefully allocating capital is actually one of the ways GM became the world’s largest car company, surging ahead of far-less disciplined competitors. After acquiring Hughes in 1985, though, this approach completely failed.

Hughes in its aerospace business wanted breakthroughs, not incremental changes to next year’s model. Hughes senior executives sometimes didn’t even know for sure how a new technology would even be sold.

Yet this wasn’t just a strictly intuitive approach. An approved project needed to solve a tough technical problem the executives understood well, so they knew a solution would almost certainly prove valuable. If management now only wanted to see R & D projects where the expected returns were easily quantified, Knott said, say goodbye to the kinds of breakthrough efforts she managed. And Hughes never recovered.

So Minnesota legislators in 1981, along with members of Congress that same year, could be forgiven for passing a tax credit to fix an innovation problem they probably didn’t understand. In the Detroit headquarters of America’s third-largest company, the MBAs didn’t really get it, either.